Architectural Patterns in AI-Assisted Code Generation: A Systematic Review
DOI:
https://doi.org/10.55204/trc.v6i2.e691Keywords:
Health in older adults, Nutritional assessment, Web application, XP methodology, Software efficiencyAbstract
Software architecture plays a fundamental role in the development of complex computing systems, as it defines high-level structural decisions that directly influence quality attributes such as maintainability, scalability, and reliability. In parallel, recent advancements in generative artificial intelligence, particularly in large language models, have driven its adoption across different phases of software engineering, primarily in automatic code generation. However, current approaches tend to focus on the implementation level, paying limited attention to architectural decisions and the explicit use of architectural patterns. This work presents a structured review of the state of the art regarding the application of generative artificial intelligence in software architecture, emphasizing the relationship between requirements, architectural decisions, and AI-assisted code generation. The methodology is based on a qualitative and comparative analysis of recent scientific literature, identifying approaches, benefits, and reported limitations. The results show that generative AI can support early architectural design, architecture-code alignment, and architectural analysis, although challenges persist, such as the lack of architectural datasets, limited interpretability, and the absence of systematic evaluation mechanisms. It is concluded that the explicit integration of architectural patterns as structured knowledge is key to improving the consistency and quality of software development assisted by artificial intelligence.Downloads
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